βοΈ Career path
AI Platform Engineer
Architect, serve, and cost-optimize enterprise AI platforms in production.
0 / 27 complete
20 labs Β· ~7.5h total
Bridge the gap between DevOps and GenAI. This path focuses not on training models but on
running them: serving with the right pattern (scale-to-zero serverless vs dedicated GPU
capacity), dynamic batching for throughput, taming cold starts, optimizing AI spend, and
assembling the whole stack into one end-to-end GenAI platform - the enterprise architect's remit.
Targets: AI Platform Engineer / Enterprise AI Architect
π³ Docker Foundation
Prerequisite foundations β skip if you already know this.
βΈοΈ Kubernetes
- 1. What is Kubernetes? (overview) β
- 2. Your First Pod β
- 3. Deployments: Scale, Roll Out & Roll Back β
- 4. Services: ClusterIP, NodePort & LoadBalancer β
- 5. Jobs and CronJobs β
- 6. Horizontal Pod Autoscaling β
- 7. Persistent volumes and StatefulSets β
- 8. ConfigMaps and Secrets β
- 9. Kubernetes - Knowledge Check β